Establishing Cause & Effect

Establishing a Cause-Effect Relationship

How do we establish a cause-effect (causal) relationship? What criteria do we have to
meet? Generally, there are three criteria that you must meet before you can say that you
have evidence for a causal relationship:

Temporal Precedence

First, you have to be able to show that your cause happened before your
effect. Sounds easy, huh? Of course my cause has to happen before the effect. Did you ever
hear of an effect happening before its cause? Before we get lost in the logic here,
consider a classic example from economics: does inflation cause unemployment? It certainly
seems plausible that as inflation increases, more employers find that in order to meet
costs they have to lay off employees. So it seems that inflation could, at least
partially, be a cause for unemployment. But
both inflation and employment rates are occurring together on an ongoing basis. Is it
possible that fluctuations in employment can affect inflation? If we have an increase in
the work force (i.e., lower unemployment) we may have more demand for goods, which would
tend to drive up the prices (i.e., inflate them) at least until supply can catch up. So
which is the cause and which the effect, inflation or unemployment? It turns out that in
this kind of cyclical situation involving ongoing processes that interact that both may
cause and, in turn, be affected by the other. This makes it very hard to establish a
causal relationship in this situation.

Covariation of the Cause and Effect

What does this mean? Before you can show that you have a causal relationship you
have to show that you have some type of relationship. For instance, consider the
syllogism:

if X then Y
if not X then not Y

If you observe that whenever X is present, Y is also present, and whenever X is absent,
Y is too, then you have demonstrated that there is a relationship between X and Y. I don't
know about you, but sometimes I find it's not easy to think about X's and Y's. Let's put
this same syllogism in program evaluation terms:

if program then outcome
if not program then not outcome

Or, in colloquial terms: if you give a program you observe the outcome but if you don't
give the program you don't observe the outcome. This provides evidence that the program
and outcome are related. Notice, however, that this syllogism doesn't not provide evidence
that the program caused the outcome -- perhaps there was some other factor present with
the program that caused the outcome, rather than the program. The relationships described
so far are rather simple binary relationships. Sometimes we want to know whether different
amounts of the program lead to different amounts of the outcome -- a continuous
relationship:

if more of the program then more of the outcome
if less of the program then less of the outcome

No Plausible Alternative Explanations

Just because you show there's a
relationship doesn't mean it's a causal one. It's possible that there is some other
variable or factor that is causing the outcome. This is sometimes referred to as the
"third variable" or "missing variable" problem and it's at the heart
of the issue of internal validity. What are some of the possible plausible alternative
explanations? Just go look at the threats to internal validity (see single
group threats, multiple group threats or social threats) -- each one describes a type of alternative
explanation.

In order for you to argue that you have demonstrated internal validity -- that you have
shown there's a causal relationship -- you have to "rule out" the plausible
alternative explanations. How do you do that? One of the major ways is with your research
design. Let's consider a simple single group threat to internal validity, a history
threat. Let's assume you measure your program group before they start the program (to
establish a baseline), you give them the program, and then you measure their performance
afterwards in a posttest. You see a marked improvement in their performance which you
would like to infer is caused by your program. One of the plausible alternative
explanations is that you have a history threat -- it's not your program that caused the
gain but some other specific historical event. For instance, it's not your anti-smoking
campaign that caused the reduction in smoking but rather the Surgeon General's latest
report that happened to be issued between the time you gave your pretest and posttest. How
do you rule this out with your research design? One of the simplest ways would be to
incorporate the use of a control group -- a group that is comparable to your program group
with the only difference being that they didn't receive the program. But they did
experience the Surgeon General's latest report. If you find that they didn't show a
reduction in smoking even though they did experience the same Surgeon General report you
have effectively "ruled out" the Surgeon General's report as a plausible
alternative explanation for why you observed the smoking reduction.

In most applied social research that involves evaluating programs, temporal precedence
is not a difficult criterion to meet because you administer the program before you measure
effects. And, establishing covariation is relatively simple because you have some control
over the program and can set things up so that you have some people who get it and some
who don't (if X and if not X). Typically the most difficult criterion to meet is the third
-- ruling out alternative explanations for the observed effect. That is why research
design is such an important issue and why it is intimately linked to the idea of internal
validity.